You can check the final result of this tutorial in this repository. Please try this tutorial in an environment where a webcam and a microphone are available. We will create a simple web-based real-time video processing app with ~10 or 20 LoC. Such web-based UIs are more friendly for users than desktop GUIs in recent days. Developers can use text inputs, sliders, or other web-based components to accept user inputs or show data.It’s convenient if we can show demos on such portable devices. Because all the users need is web browsers, the users can use the apps on their smartphones.It does not require any set-ups or external dependencies. The users can use the apps only by accessing them through web browsers.If we deploy the apps on the cloud, we can share the apps with our users simply by sending the URLs.
waitKey ( 1 ) & 0xFF = ord ( 'q' ) : breakĬompared to the GUI apps like above using cv2.VideoCapture and cv2.imshow that run on local environments, web-based apps have some advantages like below. Canny (frame, 100, 200 ) # Some image processingĬv2. I had a presentation about this topic at EuroPython 2022 titled as “Real-time browser-ready computer vision apps with Streamlit.”Ĭap = cv2. In addition, you can try the examples above on your local by following the instructions at the examples section. While those data are only processed on memory and not saved to any storage, however, if you are concerned, please do not use them.Īs for the following contents in this article, we can execute all of them on our local. These sample apps are hosted on the public cloud ( Streamlit Cloud), and the video and audio streams are transmitted to and processed at the cloud server. You can see more examples at the examples section. Users can change model type and model parameters interactively during execution. Online demo🎈Ī sample web-based style transfer app. Users can change the threshold interactively during execution. This tech stack is useful for creating demos and prototyping ideas of video/audio apps such as human or object detection, style transfer, image filters, speech recognition, video chat apps, and more.Ī sample web-based object detection app. Since such apps are web-based, they can be deployed to the cloud, shared with users easily, and have modern and user-friendly UIs. On top of it, developers can make real-time video/audio processing apps that receive video/audio streams from users’ media devices, only with ~10 lines of code in the case of the simplest example. Streamlit is a Python framework with which developers can quickly build web apps without frontend coding. In this article, we will see how we can create browser-ready real-time video/audio processing apps with Streamlit. Coi munje Developing web-based real-time video/audio processing apps quickly with Streamlit
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